Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification

• URL: http://arxiv.org/abs/2605.08121v1
• Date: Tue, 28 Apr 2026 13:21:18 GMT
• Title: Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification
• Abstract summary: We present a design-space exploration of hierarchical federated learning architectures for plant disease classification.<n>We focus on the trade-offs between predictive performance and energy efficiency.<n>We introduce a power- and energy-aware optimization framework that enables the systematic evaluation and selection of model-aggregator configurations.
• Score: 3.265862388280526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Early detection of plant diseases is critical for improving crop productivity, while it also facilitates the foundations of precision agriculture. Recent advances in distributed deep learning have enabled plant disease classification models to be trained across geographically distributed agricultural sensing infrastructures. However, deploying such systems in large-scale Internet of Things (IoT) environments, introduces significant challenges related to computational cost, energy consumption, and system efficiency. In this paper, we present a design-space exploration of hierarchical federated learning architectures for plant disease classification, with a particular focus on the trade-offs between predictive performance and energy efficiency. We further introduce a power- and energy-aware optimization framework that enables the systematic evaluation and selection of model-aggregator configurations under varying deployment constraints. The hierarchical federated architecture organizes distributed clients through intermediate aggregation layers, reducing communication and computational overhead. We evaluate multiple convolutional neural network architectures, including EfficientNet-B0, ResNet-50, and MobileNetV3-Large, in combination with different federated aggregation strategies such as FedAvg, FedProx, and FedAvgM. Experimental results demonstrate that different model-aggregator combinations exhibit distinct performance-energy trade-offs. Consequently, we highlight configurations that achieve competitive diagnostic accuracy and significantly reduce system resource requirements.

Related papers

• Constrained Bayesian Optimization for Hierarchical Federated Learning in IoT Networks for Plant Disease Classification [3.157511963455073]
  The deployment of Hierarchical Federated Learning (HFL) in resource-constrained Internet of Things (IoT) environments requires careful configuration to balance predictive performance with energy consumption and execution time.<n>This paper presents a constrained Bayesian Optimization framework for the efficient configuration of HFL deployments.<n>A weighted objective function captures user-defined trade-offs among energy consumption, execution time, and predictive performance, while explicit constraints ensure compliance with deployment-specific resource and accuracy requirements.
  arXiv  Detail & Related papers  (2026-09-06T20:56:40Z)
• Power Grid Control with Graph-Based Distributed Reinforcement Learning [60.49805771047161]
  This work advances a graph-based distributed reinforcement learning framework for real-time, scalable grid management.<n>A Graph Neural Network (GNN) is employed to encode the network's topological information within the single low-level agent's observation.<n>Experiments on the Grid2Op simulation environment show the effectiveness of the approach.
  arXiv  Detail & Related papers  (2025-09-02T22:17:25Z)
• Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environment [11.034856549533885]
  Federated learning (FL) has emerged as a promising paradigm within edge computing (EC) systems.<n>We propose Hat-DFed, a Heterogeneous-aware and coset-effective decentralized federated learning (DFL) framework.
  arXiv  Detail & Related papers  (2025-08-01T03:07:32Z)
• Graph Neural Networks for Electricity Load Forecasting [1.0797981721308225]
  Graph Neural Networks (GNNs) have emerged as a powerful paradigm to model spatial dependencies in load data.<n>This paper introduces a comprehensive framework that integrates graph-based forecasting with attention mechanisms and ensemble aggregation strategies.
  arXiv  Detail & Related papers  (2025-07-04T16:18:18Z)
• Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems [58.95962217043371]
  We present a causal framework to analyze how agent outputs, whether correct or erroneous, propagate under topologies with varying sparsity.<n>Our empirical studies reveal that moderately sparse topologies, which effectively suppress error propagation while preserving beneficial information diffusion, typically achieve optimal task performance.<n>We propose a novel topology design approach, EIB-leanrner, that balances error suppression and beneficial information propagation by fusing connectivity patterns from both dense and sparse graphs.
  arXiv  Detail & Related papers  (2025-05-29T11:21:48Z)
• Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments [25.405210975577834]
  This paper proposes a clustering strategy that leverages prior knowledge similarity to group devices with similar data and communication characteristics.<n>A novel Cluster- Aware Multi-round Update (CAMU) strategy is proposed, which treats clusters as the basic units and adjusts the local update frequency based on the clustered contribution threshold.
  arXiv  Detail & Related papers  (2025-05-06T02:48:48Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation.<n> deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency.<n>This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization [82.12796238714589]
  We propose a novel cloud radio access network (Cloud-RAN) based vertical FL system to enable fast and accurate model aggregation. We characterize the convergence behavior of the vertical FL algorithm considering both uplink and downlink transmissions. We establish a system optimization framework by joint transceiver and fronthaul quantization design, for which successive convex approximation and alternate convex search based system optimization algorithms are developed.
  arXiv  Detail & Related papers  (2023-05-04T09:26:03Z)
• Beyond Multilayer Perceptrons: Investigating Complex Topologies in Neural Networks [0.12289361708127873]
  We explore the impact of network topology on the approximation capabilities of artificial neural networks (ANNs) We propose a novel methodology for constructing complex ANNs based on various topologies, including Barab'asi-Albert, ErdHos-R'enyi, Watts-Strogatz, and multilayer perceptrons (MLPs) The constructed networks are evaluated on synthetic datasets generated from manifold learning generators, with varying levels of task difficulty and noise, and on real-world datasets from UCI.
  arXiv  Detail & Related papers  (2023-03-31T09:48:16Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.